Clinical predictive factors and prediction models for end‐stage renal disease in Chinese patients with type 2 diabetes mellitus

Dear Editor Diabetesmellitus (DM)has become a significant chronic condition that seriously affects human health.1 Nowadays, China has become the country with the largest number of DM patients worldwide, of which more than 90% are type 2 diabetes mellitus (T2DM).2 The increasing prevalence of DM exacerbates the incidence of end-stage renal disease (ESRD).3 T2DM-related ESRD not only reduces survival rate and health-related quality of life but also places a significant cost on patients as well as society.4–6 To identify clinical predictive factors and develop prediction models for ESRD risk in T2DM patients, we used the study population extracted from the China Renal Data System, a database containing the information of more than seven million patients attended at 19 hospitals in the Chinese mainland, as previously described.7 ESRD, including an eGFR of 15 mL/min/1.73 m2 or less, or the commencement of dialysis or kidney transplantation due to ESRD, was classified as the outcome. Eventually, clinical data of adult patients with T2DM were collected from 17 hospitals. Using a randomized approach, 55 824 patients with T2DM from 10 medical centers were included in the derivation cohort, and 25 745 patients from seven additional medical institutions were included for external validation. The patient selection flowchart is shown in Figure S1. After a median of 384 (123, 900) days of followup, there were 1,527 (2.74%) outcomes in the derivation cohort (n = 55,824). Table S1 summarizes the clinical features at baseline. Spearman correlation analysis was conducted to identify the correlation between continuous variables (Figure S2), and variables with higher average correlation (correlation ≥ 0.5) were removed. Univariate Cox regression analysis was used to select potential


Dear Editor
Diabetes mellitus (DM) has become a significant chronic condition that seriously affects human health. 1 Nowadays, China has become the country with the largest number of DM patients worldwide, of which more than 90% are type 2 diabetes mellitus (T2DM). 2 The increasing prevalence of DM exacerbates the incidence of end-stage renal disease (ESRD). 3 T2DM-related ESRD not only reduces survival rate and health-related quality of life but also places a significant cost on patients as well as society. [4][5][6] To identify clinical predictive factors and develop prediction models for ESRD risk in T2DM patients, we used the study population extracted from the China Renal Data System, a database containing the information of more than seven million patients attended at 19 hospitals in the Chinese mainland, as previously described. 7 ESRD, including an eGFR of 15 mL/min/1.73 m 2 or less, or the commencement of dialysis or kidney transplantation due to ESRD, was classified as the outcome. Eventually, clinical data of adult patients with T2DM were collected from 17 hospitals. Using a randomized approach, 55 824 patients with T2DM from 10 medical centers were included in the derivation cohort, and 25 745 patients from seven additional medical institutions were included for external validation. The patient selection flowchart is shown in Figure S1.
After a median of 384 (123, 900) days of followup, there were 1,527 (2.74%) outcomes in the derivation cohort (n = 55,824). Table S1 summarizes the clinical features at baseline. Spearman correlation analysis was conducted to identify the correlation between continuous variables ( Figure S2), and variables with higher average correlation (correlation ≥ 0.5) were removed. Univariate Cox regression analysis was used to select potential # Yueming Gao and Zhi Shang contributed equally to this work and share first authorship.  Table S2. All potential predictors were, therefore, fitted into a multivariable Cox regression model, utilizing step-wise backward selection (p < 0.05). Ten clinical predictive factors, including age, hypertension, diabetes retinopathy (DR), hemoglobin (HGB), serum albumin (ALB), serum creatinine (Scr), serum uric acid, Low-density lipoprotein cholesterol (LDL-C), serum fibrinogen, and urinary protein were selected into the final model (Table S3). We constructed three clinical prediction models using various combinations of predictors selected by multivariable Cox regression (Table 1) Figure 1A). In addition, these models attained satisfactory calibration, as shown in Figure S3A-C. Internal validation using bootstrapping also achieved a robust discrimination, with an AUC of 0.914-0.927, as shown in Table 1.
In the external validation cohort (n = 25,745), during a median follow-up of 321 (90, 758) days, there were 1,084 (4.21%) outcomes. Table S4 presents the baseline clinical features. Based on the receiver-operating characteristic (ROC) curves, the prediction models achieved an AUC ranging from 0.868 to 0.882 ( Figure 1B). As seen in Figure S3D-F, these models also attained satisfactory calibration.
In conclusion, using a large multi-center retrospective cohort in the Chinese mainland, we identified 10 clinical predictive factors and developed models to predict ESRD in T2DM patients, which showed excellent prediction performance. To the best of our knowledge, we have established models to predict ESRD based on the largest population of T2DM patients in the Chinese Mainland. These prediction models were further provided as simple bedside tools, including a risk score and a nomogram, which could be extensively applied to assess T2DM patients' ESRD risk in clinical practice, to aid clinical decision-making and sensible resource allocation.

A C K N O W L E D G E M E N T S
The authors wish to thank the clinicians and healthcare professionals at the participating centers in the CRDS Study. Yongxiang Gao and the team of Digital Health China Technologies Co., LTD deserve special gratitude for their assistance with data extraction. This study was supported by the National Key Research and Development Program of China to Prof. Bicheng Liu as PI (grant number: 2018YFC1314000).

C O N F L I C T O F I N T E R E S T S TAT E M E N T
The authors declare no conflict of interest.

D ATA AVA I L A B I L I T Y S TAT E M E N T
The data that support the findings of this study are accessible from the corresponding author upon request. The data are not publicly available owing to ethical or privacy concerns. TA B L E 2 A risk score of model 1(full model).

Predictors Category Point
Age (